Survey of optical indoor positioning systems

Mautz, Rainer; Tilch, Sebastian

doi:10.1109/ipin.2011.6071925

Cited by 259 publications

(167 citation statements)

References 19 publications

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“…Kim & Jun, 2008). The study of Mautz and Tilch (2011) noted that the server transmits the recognised location information to the mobile device; hence, enabling real-time positioning and navigation.…”

Section: Augmented Reality (Ar) Methodsmentioning

confidence: 99%

“…In general, the magnetic positioning system has high accuracy and does not suffer from NLOS errors between magnetic sensors and the tracked object (Gu et al, 2009;Chung et al, 2011;Mautz & Tilch, 2011). However, one issue with the magnetic positioning system is that of limited coverage (Mautz, 2012;B.…”

Section: Challenges and Drawbacks Of Magnetic Positioningmentioning

confidence: 99%

“…The Optical and Vision-based positioning system is a system where the position of a person or an object is determined in a building by identifying a marker or image that is within view, with the aid of a mobile sensor or camera in a mobile device carried by the user (Mautz & Tilch, 2011;Klopschitz, Schall, Schmalstieg, & Reitmayr, 2010). The marker is a fixed object that has patterns used as a point-of-interest, or reference within the field of view of an imagery sensor such as a mobile camera (Mautz, 2012).…”

Section: Optical/vision Positioning Systemmentioning

confidence: 99%

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A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

Sakpere

Oshin

Mlitwa

2017

SACJ

150

129

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The research and use of positioning and navigation technologies outdoors has seen a steady and exponential growth. Based on this success, there have been attempts to implement these technologies indoors, leading to numerous studies. Most of the algorithms, techniques and technologies used have been implemented outdoors. However, how they fare indoors is different altogether. Thus, several technologies have been proposed and implemented to improve positioning and navigation indoors. Among them are Infrared (IR), Ultrasound, Audible Sound, Magnetic, Optical and Vision, Radio Frequency (RF), Visible Light, Pedestrian Dead Reckoning (PDR)/Inertial Navigation System (INS) and Hybrid. The RF technologies include Bluetooth, Ultra-wideband (UWB), Wireless Sensor Network (WSN), Wireless Local Area Network (WLAN), Radio-Frequency Identification (RFID) and Near Field Communication (NFC). In addition, positioning techniques applied in indoor positioning systems include the signal properties and positioning algorithms. The prevalent signal properties are Angle of Arrival (AOA), Time of Arrival (TOA), Time Difference of Arrival (TDOA) and Received Signal Strength Indication (RSSI), while the positioning algorithms are Triangulation, Trilateration, Proximity and Scene Analysis/ Fingerprinting. This paper presents a state-of-the-art survey of indoor positioning and navigation systems and technologies, and their use in various scenarios. It analyses distinct positioning technology metrics such as accuracy, complexity, cost, privacy, scalability and usability. This paper has profound implications for future studies of positioning and navigation.

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Section: Augmented Reality (Ar) Methodsmentioning

confidence: 99%

Section: Challenges and Drawbacks Of Magnetic Positioningmentioning

confidence: 99%

Section: Optical/vision Positioning Systemmentioning

confidence: 99%

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A State-of-the-Art Survey of Indoor Positioning and Navigation Systems and Technologies

Sakpere

Oshin

Mlitwa

2017

SACJ

150

129

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“…A literature review on optical systems for indoor positioning has been published by Mautz and Tilch in 2011 [19]. All camera-based positioning systems deal with the definition of position and rotations in a 3D world when the primary observation is a 2D position on a camera sensor.…”

Section: State Of the Artmentioning

confidence: 99%

An Original Application of Image Recognition Based Location in Complex Indoor Environments

Chiabrando

Pietra

Lingua

et al. 2017

IJGI

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This paper describes the first results of an image recognition based location (IRBL) for a mobile application focusing on the procedure to generate a database of range images (RGB-D). In an indoor environment, to estimate the camera position and orientation, a prior spatial knowledge of the surroundings is needed. To achieve this objective, a complete 3D survey of two different environments (Bangbae metro station of Seoul and the Electronic and Telecommunications Research Institute (ETRI) building in Daejeon, Republic of Korea) was performed using a LiDAR (Light Detection and Ranging) instrument, and the obtained scans were processed to obtain a spatial model of the environments. From this, two databases of reference images were generated using specific software realised by the Geomatics group of Politecnico di Torino (ScanToRGBDImage). This tool allows us to generate synthetically different RGB-D images centred in each scan position in the environment. Later, the external parameters (X, Y, Z, ω, ϕ, and κ) and the range information extracted from the retrieved database images are used as reference information for pose estimation of a set of acquired mobile pictures in the IRBL procedure. In this paper, the survey operations, the approach for generating the RGB-D images, and the IRB strategy are reported. Finally, the analysis of the results and the validation test are described.

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“…Video cameras have long been used for recording body movements and for positioning purposes [19,52]. As a result, efficient algorithms for detecting human body movements from a video stream have been available for some decades.…”

Section: Related Work With Other Technologiesmentioning

confidence: 99%